Basic Course in Statistics

Teaching semester

Autum and spring

Objectives and Content

Objectives:

To give an introduction to probability theory and statistical methods, with emphasis on the former

Content:

The main emphasis in this course is on probability models. Discrete and continuous distributions, among others the binomial, the hypergeometric, the exponential, the Poisson and the normal distributions are treated. Joint probability distributions and correlation are also covered. Examples are given from many areas. The last part of the course deals with principles for estimating unknown quantities using maximum likelihood, with confidence intervals, and with hypothesis testing.

Learning Outcomes

On completion of the course the student should have the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

The student

• Fundamental concepts in probability, such as expectation, variance and correlation.
• Discrete and continuously distributed random variables
• Law of large numbers and the central limit theorem
• Joint and conditional distributions
• Parameter estimation and confidence intervals
• Hypothesis tests and p-values

General competence

The student

• Has a practical understanding of the probability concept as it used broadly in society
• Perform and interpret statistical analyses

Recommended Previous Knowledge

MAT101, MAT105 or MAT111 (can be taken together with STAT110).

Credit Reduction due to Course Overlap

STAT101: 5 ECTS, ECON240: 4 ECTS

Compulsory Assignments and Attendance

Excercises

Forms of Assessment

Written examination early in semester, 5 hours.

Written examination late in semester: 4 hours

Grading Scale

The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.